Data-driven Probabilistic Trajectory Learning with High Temporal Resolution in Terminal Airspace
This work addresses trajectory prediction for air traffic management, but it appears incremental as it builds on existing methods with specific improvements.
The paper tackles flight trajectory prediction by proposing a data-driven learning framework that combines mixture models and seq2seq-based neural networks to address error propagation and dimensionality reduction, resulting in improved long-step prediction accuracy and higher temporal resolution (1 timestep per second vs. 0.1 timestep per second) compared to state-of-the-art methods on a terminal airspace dataset.
Predicting flight trajectories is a research area that holds significant merit. In this paper, we propose a data-driven learning framework, that leverages the predictive and feature extraction capabilities of the mixture models and seq2seq-based neural networks while addressing prevalent challenges caused by error propagation and dimensionality reduction. After training with this framework, the learned model can improve long-step prediction accuracy significantly given the past trajectories and the context information. The accuracy and effectiveness of the approach are evaluated by comparing the predicted trajectories with the ground truth. The results indicate that the proposed method has outperformed the state-of-the-art predicting methods on a terminal airspace flight trajectory dataset. The trajectories generated by the proposed method have a higher temporal resolution(1 timestep per second vs 0.1 timestep per second) and are closer to the ground truth.